1. Field of the Invention
The present invention is directed toward a new method that combines interpolation between neighboring A-lines with cross-correlation for high precision estimation of the transverse displacement. Due to this high precision transverse estimation, the method of the present invention can produce quality transverse-elastograms that display the transverse component of the strain tensor. These higher precision transverse displacement estimates also allow a finer correction for the transverse decorrelation that corrupts the axial estimation. The method of the present invention may be employed to divide the transverse-elastogram by the axial-elastogram on a pixel-by-pixel basis, in order to produce a new image that displays the distribution of strain ratios in the tissue.
2. Description of the Prior Art
Prior art references referred to herein include:
(1) Alam, S. K.; Ophir, J.; Konofagou, E. An adaptive strain estimator for Elastography, IEEE Trans. Ultrason., Ferroel., Freq. Cont.; 1998; PA0 (2) Bamber J. C.; Bush, N. L. Freehand elasticity imaging using speckle decorrelation rate. Acoustical imaging 22: 285-292; 1996; PA0 (3) Bendat J. S.; Piersol, A. G. Random Data, Analysis and Measurement Procedures, John Wiley & Sons, New York, 2nd edition; 1986; PA0 (4) Bohs L. N.; Trahey, G. E. A novel method for angle independent ultrasonic imaging of blood flow and tissue motion. IEEE Trans. Biomed. Eng. 38: 280-286; 1991; PA0 (5) Bohs L. N.; Friemel B. H.; Trahey, G. E. Experimental velocity profiles and volumetric flow via two-dimensional speckle tracking 21(7): 885-898; 1995; PA0 (6) Bonnefous, O. Measurement of the complete (3D) velocity vector of blood flows. Proc. IEEE Ultrason. Symp: 795-799; 1988; PA0 (7) Bonnefous, O. Statistical Analysis and time processes applied to velocity measurement. Proc. IEEE Ultrason. Symp: 887-892; 1989; PA0 (8) Burckhardt, C. B. Speckle in Ultrasound B-Mode Scans. IEEE Trans. on Son. and Ultras. SU-25 (1): 1-6; 1978; PA0 (9) Cespedes, I. and Ophir, J. Reduction of image noise in elastography. Ultrasonic Imaging 5 (2): 89-102; 1993; PA0 (10) Chaturvedi P. Insana M. Hall T. 2D companding for noise reduction in strain imaging. IEEE Trans of Ultras., Ferroel. and Freq. Control; 1998; PA0 (11) Chaturvedi P. Insana M. Hall T. Bilgen, M. 3D companding using linear arrays for improved strain imaging. IEEE Ultras. Symposium; 1997; PA0 (12) Cohn, N. A. Emelianov, S. Y. Lubinski, A. M. O'Donnell M. An Elasticity Microscope, Part I: Methods, IEEE Trans of Ultras., Ferroel. and Freq. Control 44(6): 1304-1319; 1997; PA0 (13) Dotti, D.; Lombardi, R.; Piazzi, P. Vectorial measurement of blood velocity by means of ultrasound. Med. Biol. Eng. Comp 30: 219-225; 1992; PA0 (14) Fung, Y. C. Connecting incremental shear modulus and Poisson's ratio of lung tissue with morphology and rheology of microstructure. Biorheology 26: 279-289; 1989; PA0 (15) Geiman, B. J.; Bohs, L. N.; Anderson M. E.; Czensak, S. P.; Trahey, G. E. 2D vector flow imaging using ensemble tracking: initial results. Ultras. Imag. 18: 61-62; 1996; PA0 (16) Hein, I. A. Multi-directional ultrasonic blood flow measurement with a triple beam lens. Proc. IEEE Ultrason. Symp.: 1065-1069; 1993; PA0 (17) Hein, I. A.; O'Brien, W. D. Current Time-domain Methods for assessing tissue motion by analysis from reflected ultrasound echoes- a review. IEEE Trans on Ultras., Ferroel, and Freq. Control UFFC-40, No.2, 1993; PA0 (18) Insana M.; Chaturvedi P.; Hall T.; Bilgen, M. 3D companding using 1.5D arrays for improved strain imaging. IEEE Ultrason. Symp.; 1997; PA0 (19) Irons, B; Ahmad, A. Techniques of finite elements, Ellis Horwood, p. 461; 1980; PA0 (20) Jensen, J. A. Estimation of blood velocities using ultrasound, Cambridge University Press; 1996; PA0 (21) Jurvelin, J. S.; Buschmann, M. D.; Hunziker, E. B. Optical and mechanical determination of Poisson's ratio of adult bovine humeral articular cartilage. J. Biomechanics 30, No.3: 235-241; 1997; PA0 (22) Kallel F. Proprietes elastiques des tissus mous a partir de I'analyse des changements spatio-temporels dans les signaux ultrasonores. PhD dissertation, Ecole Polytechnique, University of Montreal, Montreal, Quebec, Canada; 1995; PA0 (23) Kallel, F.; Bertrand, M. Tissue elasticity reconstruction using linear perturbation method. IEEE Trans. Med. Imag 15: 299-313; 1996; PA0 (24) Kallel, F.; Ophir, J. Three-dimensional tissue motion and its effect on image noise in elastography. IEEE Trans of Ultras., Ferroel. and Freq. Control 44:1286-1296; 1997a; PA0 (25) Kallel, F.; Varghese, T.; Ophir, J.; Bilgen, M. The nonstationary strain filter in elastography, Part II: Lateral and elevational decorrelation. Ultrasound in Med. and Biol. 23 (9): 1357-1369; 1997; PA0 (26) Kaliel, F.; Ophir, J. Elastographic imaging of low contrast elastic modulus distributions in tissue. IEEE Trans of Ultras., Ferroel. and Freq. Control; 1997b; PA0 (27) Kallel, F.; Bertrand, M.; Ophir, J. Fundamental limitations on the contrast-transfer efficiency in elastography: An analytic study. Ultras. in Med. and Biol 22(4): 463-470; 1996; PA0 (28) Kaliel, F.; Ophir, J. A least squares estimator for elastography. Ultrasonic Imaging; 1997d; PA0 (29) Kino, G. S.; Acoustic waves: devices, imaging and analog signal processing. Prentice Hall; 1987; PA0 (30) Lai W. M.; Rubin D.; Krempl, E. Introduction to Continuum Mechanics. Pergamon Press; 1978; PA0 (31) Lubinski, A. M.; Emelianov, S. Y.; Raghavan, K. R.; Yagle, A. E.; Skovoroda, A. R.; O'Donnell, M. Lateral displacement estimation using tissue incompressibility. IEEE Trans of Ultras., Ferroel. and Freq. Control 43(2); 1996; PA0 (32) Mridha, M.; Odman S. Noninvasive method for assessment of subcutaneous edema. Med. and Biol. Engineering and Computing 24: 393-398; 1986; PA0 (33) Ophir, J.; Johnson, W.; Yazdi, Y.; Shattuck, D.; Mehta, D. Correlation artifacts in speed of sound estimation in scattering media. Ultras. in Med. and Biol. 15 (4): 341-353; 1989; PA0 (34) Ophir, J.; Cespedes, I.; Ponnekanti, H.; Yazdi, Y.; Li, X. Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrasonic Imaging 13: 111-134; 1991; PA0 (35) Ophir, J; Cespedes, I; Garra, B.; Ponnekanti, H; Huang, Y; Maklad, N; Elastography: ultrasonic imaging of tissue strain and elastic modulus in vivo, European Journal of Ultrasound 3: 49-70;1996; PA0 (36) Ophir, J.; Kallel, F.; Varghese, T.; Bertrand, M.; Cespedes, I.; Ponnekanti, H. Elastography: A systems approach; The Int. J. Imag. Sys. Tech., John Wiley & Sons, Inc 8: 89-103; 1997; PA0 (37) Ramamurthy, B. S.; Trahey, G. E. Potential and limitations of angle-independent flow detection algorithms using radio-frequency and detected echo signals. Ultrasonic imaging 13: 252-268; 1991; PA0 (38) Rice, J. R.; Cleary, M. P. Some basic stress diffusion solutions for fluid-saturated elastic porous media with compressible constituents. Reviews of geophysics and space physics 14(2): 227-241; 1976; PA0 (39) Saada, A. S. Elasticity, theory and applications. New York:Pergamon Press: 377-399; 1989; PA0 (40) Sarvazyan, A. P. Low frequency acoustic properties of biological tissues. Mechanics of polymers 4:691-695; 1975; PA0 (41) Skovoroda, A R; Emelianov, S Y; Lubinski, A M; Sarvazyan, A. P.; O'Donnell, M. Theoretical analysis and verification of ultrasound displacement and strain imaging. IEEE Trans of Ultras., Ferroel. and Freq. Control 41(3); 1994; PA0 (42) Skovoroda, A. R.; Emelianov, S. Y.; O'Donnell, M. Tissue elasticity reconstruction based on ultrasonic displacement and strain images. IEEE Trans of Ultras., Ferroel. and Freq. Control 42: 747-765; 1995; PA0 (43) Sumi C.; Suzuki A.; Nakayama K. Estimation of shear modulus distribution in soft tissue from strain distribution. IEEE Trans Biomed Eng 42:193-202; 1995; PA0 (44) Trahey, G. E. Allison, J. W. von Ramm, O. T. Angle independent ultrasonic detection of blood flow. IEEE Trans. Biomed. Eng. BME-34: 965-967; 1987; PA0 (45) Varghese, T.; Ophir, J. Enhancement of echo-signal correlation in elastography using temporal stretching. IEEE Trans on Ultras., Ferroel, and Freq. Control UFFC-44, No.1: 175-180; 1997a; PA0 (46) Varghese, T.; Ophir, J. A theoretical framework for performance characterization of elastography. IEEE Trans. Ultrason. Ferroel. Freq. Cont. 44: 164-172; 1997b; PA0 (47) Wagner, R. F.; Smith, S. W.; Sandrik, J. M. Lopez, H. Statistics of Speckle in Ultrasound B-Scans. IEEE Trans. on Son. and Ultras. 30, No.3: 156-163; 1983; PA0 (48) Wagner, R. F.; Insana M. F.; Smith, S. W. Fundamental correlation lengths of coherent speckle in medical ultrasonic images. IEEE Trans on Ultras., Ferroel, and Freq. Control UFFC-35(1); 1988; and PA0 (49) U.S. Pat. No. 5,000,184 to Bonnefous.
A major disadvantage of the current practice of elastography is that only the axial component of the strain is used to produce the axial-elastogram. The lateral and elevational components are basically disregarded, yet they corrupt the axial strain estimation by inducing decorrelation noise. In fact, several papers (Lubinski et al. 1996; Cohn et al. 1997; Kallel 1995) in the field disclose or suggest that precision lateral displacement estimation cannot be achieved in the lateral direction. They had neglected the fact that the RF, instead of the envelope of the signal could be tracked.
In elastography, the axial component of the strain tensor is computed by taking the gradient of the axial (along the beam propagation axis) displacement occurring after a quasi-static tissue compression (Ophir et al. 1991; Ophir et al. 1996; Ophir et al. 1997). The estimation of the axial displacement is achieved by using time-delay estimation techniques applied to pre- and postcompression RF A-lines (Ophir et al. 1997). In general, however, the tissue motion that occurs during compression is three-dimensional.
Since the lateral (perpendicular to the beam propagation axis and in the scan plane) and elevational (perpendicular to the beam propagation axis and to the scan plane) motions are not measured, two major drawbacks are encountered. First, the axial-elastogram takes into account only a small part of the mechanical tissue motion information. Second, the unaccounted-for lateral and elevational motions are the primary causes of signal decorrelation (Kallel and Ophir 1997a; Kallel et al. 1997). The lateral or elevational decorrelation can be prevented by appropriate confinement of the tissue under study (Kallel and Ophir 1997a). Confinement may not, however, always be practical in clinical applications, especially when the tissues under study are not easily accessible.
Many publications have dealt with the problem of motion estimation in two-dimensions. Currently, most motion estimators are used to estimate the projection of the motion vector onto the ultrasound beam direction, or to measure only the axial component of the total motion. Initially, multi-dimensional motion estimation started in the field of blood velocity estimation, in order to estimate velocity components, other than the axial one. Later, in the field of elasticity imaging, some of the methods developed in the flow measurement field were borrowed while others developed new techniques, better suitable for elasticity measurements. As detailed below, these fields are different in many ways so that motion estimation algorithms should be shared with caution.
A) Blood flow-velocity estimation
Most of the problems in this field arise from the fact that the scatterers (mainly, the blood cells) to be tracked are fast-moving and their local density as well as orientation are constantly changing during blood flow (Hein and O'Brien 1993). The pulse repetition frequency and the computation speed of the algorithm used for the velocity estimation are, therefore, the most crucial factors, since a high acquisition and/or computation time can significantly limit the maximum velocity that can be measured (Hein and O'Brien 1993; Trahey et al. 1988). Also, given that the blood scatterers have a low echogenicity, the reflected echoes suffer from very low signal-to-noise ratio (SNR). Other limitations include the fact that the adjacent beams have to be as close to parallel as possible in order to estimate the same velocity component as the scatterers move across the beams (Jensen 1996). There have been various techniques proposed for two-dimensional velocity estimation and a few representative ones are mentioned below.
1) Multiple beam approach
Bonnefous (1988) discloses a 3D theoretical blood flow model that included various correlation algorithms and the use of linear interpolation between adjacent signals to detect all three motion components. Lateral beams from different pulse excitations are crosscorrelated to find the lateral displacement of the scatterers between emissions. The model was tested using only simulated data while no experimental results have been reported. Dotti et al. (1992) used a single-element transducer and extended the 1D correlation method into two dimensions using a two-element transducer. Again, by correlating signals between adjacent elements they were able to estimate the lateral flow velocity component. However, the range of lateral velocities that could be accurately measured was limited by having only two lateral measurement positions. Hein (1993) developed a system with a triple-beam ultrasound lens forming three parallel beams, that was able to measure high velocities at large depths. The lateral component was calculated when the blood scatterers would move from beam to beam. The difficulty in the implementation of this method lies in making these beams equal and parallel (Jensen 1996).
2) Speckle tracking method
An alternative to using multiple beams is the speckle tracking method. Trahey et al. (1987) have suggested using the normalized cross-covariance between small rectangles (or kernels) of two successive B-mode frames. The location of the peak in the covariance estimate indicates the amount of displacement of the speckle pattern between frames and is converted to velocity from the knowledge of the time lapse between the two frames. A major disadvantage of this technique is the large amount of calculation required in real-time, making this an impractical algorithm for blood flow measurements.
Therefore, Bohs and Trahey (1991) suggested a faster algorithm that calculates the absolute sum of the pixel differences, or the Sum-of-Absolute-Differences (SAD) algorithm. A comparison of the accuracy of tracking between correlation and SAD showed that both techniques are equally able to track scatterers with comparable accuracy, for both lateral and axial displacements. Also, as expected, it has been shown that RF processing performs more precise tracking than envelope-detected processing (Ramamurthy and Trahey 1991; Bohs and Trahey 1991). More recently, Geiman et al. (1996) have developed a method of ensemble tracking that involves parallel receive processing, 2D pattern matching, and interpolation of the resulting tracking grid to estimated sub-pixel speckle translations between successive ultrasonic acquisitions.
B) Elasticity estimation
Elastography uses a quasi-static compression to induce the strain in the ultrasonically scanned tissue (Ophir et al. 1991). The limitations of the motion estimation in this field are quite different from the ones in the blood flow estimation. Fast acquisition and/or computation may not be as important because the scatterer motion is small and can be controlled by the applied compression. Furthermore, the sonographic signal-to-noise ratio is higher in tissue than in blood by orders of magnitude. Both these factors allow us to aim for high precision of motion estimation and, thus, high elastographic signal-to-noise ratio. While high precision tracking of axial motion has been reported (Ophir et al. 1991; Skovoroda et al. 1994), precision lateral tracking has not been reported.
The speckle tracking method developed by Bohs and Trahey (1991) was used to study the mechanical vibration propagating through tissue (Walker et al. 1992). A similar method, called 2D companding, was recently developed for elastography in order to reduce decorrelation noise in elastograms using applied axial strains of up to 5% and correcting for the lateral decorrelation, if out-of-plane displacement was negligible (Chaturvedi et al. 1998). This method uses speckle block matching techniques (Bohs and Trahey 1991) to track the 2D motion of the scatterers after static compression. It has recently been extended to 3D companding (Chaturvedi et al. 1997) in order to compensate for the elevational displacement as well. The main limitation of this method is that it cannot be used to produce quality lateral elastograms (Chaturvedi et al. 1998), since it is limited by the coarse lateral spacing between elements (defined as pitch) of the ultrasonic array to estimate the lateral displacement. For precision lateral displacement estimation, much finer tracking must be performed. Chaturvedi et al. (1998) attempted to perform interpolation using the Grid Slopes technique described by Geiman et al. (1996) but the gradient calculation involved in the slope estimation introduced large errors.
Lubinski et al. (1996) have shown that when complex baseband data are used to track lateral motion, the variance of the lateral displacement estimation is larger than the variance of the axial displacement estimate by orders of magnitude. They show that lateral displacement images are noisy after using the traditional 2D correlation tracking. Based on these results, they proposed the estimation of lateral displacement by making the assumption of incompressibility of the tissue, and thus making use of the precision of the axial measurement. It is, however, rare that these assumptions are valid at all spatial scales for biological tissues (Chaturvedi, et al. 1998).